Embodiments of the present specification relate generally to image reconstruction, and more particularly to systems and methods for reconstruction of undersampled magnetic resonance images.
Imaging techniques such as Magnetic Resonance Imaging (MM), require longer duration for raw data acquisition and high computation power for reconstruction of images from acquired raw data. Parallel imaging and compressed sensing techniques have been used to reduce MRI scan time by undersampling of k-space. Different spatial sensitivities of coil arrays and/or sparsity in the transform domain are exploited for undersampling of data during image acquisition. However, conventional MRI techniques provide reconstructed images with good image quality only for smaller undersampling factors, and image artifacts are pronounced when higher undersampling factors are employed.
Deep learning networks can be used for magnetic resonance (MR) image reconstruction from undersampled acquisition data. However, increasing the number of convolutional layers in deep neural networks may not improve the quality of the reconstructed image due to ineffective feature propagation. In compressed sensing methods using wavelets or total variation, the relative strengths of the sparsity and data consistency terms are adjusted in the cost function. When the sparsity term is too weak, residual aliasing is introduced in the reconstructed images. When the sparsity term is relatively strong, the reconstructed image appears unnaturally flat.
Acquisition of highly undersampled raw data in k-space enables faster imaging. However, the quality of the reconstructed image based on undersampled raw data must be comparable to fully sampled MR images. Image reconstruction techniques configured to generate an estimate of fully sampled MR images based on undersampled raw data in k-space are required. Specifically, newer architectures for convolution based deep learning networks are desirable.